As a fast and scalable framework, Apache Hadoop makes it easier for data scientists to store, process, and analyze very large volumes of data. Many data scientists use Hadoop for offline or batch processing. They can further accelerate big data analysis by scaling up the framework by adding nodes in the cluster. Azure HDInsight enables users to process a massive amount of data using a number of open source frameworks including Apache Hadoop.
Microsoft has designed Azure HDInsight as a cloud-based service for processing and analysis of large volumes of streaming and historical data. The enterprises can further use HDInsight as a fully-managed and full-spectrum analytics service. HDInsight enables developers to build big data applications using open source frameworks like Apache Hadoop, Apache Spark, Apache Hive, Apache Kafka, Apache LLAP, Apache Storm, and Microsoft Machine Learning Server.
It further comes with features to simplify data extraction, data transformation, data loading, and data warehousing. The HDInsight supports hottest technological trends like the Internet of Things (IoT) and machine learning. Hence, the businesses can use HDInsight to build custom big data applications and process a massive amount of data. But it is always important for the users to understand the important aspects of Azure HDInsight.
Understanding Important Aspects of Azure HDInsight
Uses Hortonworks Data Platform
Microsoft has designed Azure HDInsight based on Hortonworks Data Platform (HDP). HDP is an open source framework which is developed based on Apache Hadoop, Apache Spark, and Apache Hive. The features and tools provided by HDP help businesses to gain actionable insights by analyzing both structured and unstructured data. The open source framework supports distributed data storage and facilitates processing of large datasets. HDP contributes hugely towards making HDInsight a robust data processing and analytics services.
Simplifies Big Data Cluster Creation and Scaling
Often developers have to focus extensively on clustering to accelerate analysis of a massive amount of time. They have to put extra time and effort to create new clusters and scale existing clusters according to the amount of big data. Azure HDInsight enables developers to create big data clusters on demand. The developers can further scale up or scale down the big data clusters easily. Hence, they can process a massive amount of data without focusing on cluster management. The option helps businesses to pay only for the resources used by them.
Apache Product Implementation
As mentioned earlier, Azure HDInsight enables developers to build custom big data solutions and process massive amounts of data using the implementation of widely used Apache products. The developers can facilitate batch processing using Apache Pig, Apache Spark, or Apache Hive. Likewise, they can access NoSQL data using Apache HBase, and stream millions of streaming events using Apache Storm, Apache Spark, or Apache Kafka. The users can further integrate Apache Spark with Hadoop MapReduce to extract, transform, and load (ETL) large data cluster on demand.
Azure Service Integration
In addition to using implementation of Apache products, the users also have the option to integrate HDInsight with a wide range of Azure data stored and services natively and seamlessly. For instance, they can integrate the cloud-based analytics service with Data Lake Storage, Azure Cosmos DB, SQL Data Warehouse, Data Factory, Blob Storage and Event Hubs. The integration helps developers to accelerate custom big data solution development and big data processing.
Azure HDInsight Tools
While using the cloud-based service, the developers can use HDInsight Interactive Query to query a massive amount of data stored in Azure storage and Azure Data Lake Storage. They can further leverage interactive query to work with a set of business intelligence (BI) tools including Apache Zeppelin, Visual Studio Code, Beeline, Microsoft Power BI, Hive ODBC, Tableau and Apache DBeaver. These tools make it easier for a user to build mission-critical big data application and process massive amounts of data by extending HDInsight.
HDInsight helps enterprises from various sectors to make informed decisions and improve customer experience. The businesses can use the cloud-based analytics service to help employees to make data-driven decisions and boost customer engagement by creating personalized recommendation engines. HDInsight further helps businesses to keep a large amount of corporate and customer data secure by providing enterprise-grade security. The manufacturing companies can use the service to maintain equipment on time and prevent unexpected failures.
As a sophisticated cloud-based service, HDInsight enables users to process massive amounts of data and build custom big data solution. Microsoft has been upgrading HDInsight at regular intervals to make it a robust big data analytics in the cloud. The upgraded version of HDInsight allows users to improve big data processing and analysis using Apache Hadoop 3.0. It even helps businesses to keep their big data applications secure by providing enterprise-grade security and compliance.